Methods and apparatus to calibrate return path data for audience measurement

ABSTRACT

Example apparatus disclosed herein include a return path data classifier to classify a first viewing period associated with segments of return path data received from a set top box into tuning classifications based on the segments of the return path data; calculate a total reported tuning duration for the first viewing period when the first viewing period is classified as live or playback tuning; and compare the total reported tuning duration to a duration threshold to determine whether the segments of return path data associated with the first viewing period are valid. The example apparatus also includes a return path data rectifier to rectify missing tuning data associated with a second viewing period based on tuning data included in the segments of return path data associated with the first viewing period when the segments of the return path data associated with the first viewing period are determined to be valid.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 16/152,115, filed Oct. 4, 2018, which claims the benefit of U.S.Provisional Patent Application Ser. No. 62/681,489, filed on Jun. 6,2018. U.S. patent application Ser. No. 16/152,115 and U.S. ProvisionalPatent Application Ser. No. 62/681,489 are hereby incorporated byreference in their entireties. Priority to U.S. patent application Ser.No. 16/152,115 and U.S. Provisional Patent Application Ser. No.62/681,489 is claimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audience measurement, and, moreparticularly, to methods and apparatus to calibrate return path data foraudience measurement.

BACKGROUND

Many households access media through set top boxes (STBs) provided bymedia providers (e.g., cable media providers, satellite media providers,etc.). Some STBs are equipped to report tuning data, which is indicativeof the media accessed by the STBs, back to the media providers. Tuningdata reported back to media providers via STBs is sometimes referred toas return path data (RPD). RPD tuning data may be used by audiencemeasurement entities to monitor people's exposure to media.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example environment including an example RPD calibrationmodule to calibrate return path data for audience measurement inaccordance with teachings disclosed herein.

FIG. 2 is an example implementation of the example RPD calibrationmodule of FIG. 1 .

FIGS. 3-6 are flowcharts representative of example machine readableinstructions that may be executed to implement the example RPDcalibration module of FIGS. 1 and/or 2 .

FIG. 7 is a schematic illustration of an example processing systemstructured to execute the example machine-readable instructions of FIGS.4-6 to implement the example RPD calibration module of FIGS. 1 and/or 2.

The figures are not to scale. In general, the same reference numberswill be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

In the past, audience measurement entities (AMEs) have relied onaudience measurement panelists to collect and/or measure audiencemeasurement data used to generate audience metrics such as, for example,media ratings indicative of the number of households tuned to particularmedia programs at a given point in time. Typically, media ratings, orsimply ratings, are expressed as a percentage of households relative toa population of interest, also referred to as a population universe. Forexample, the population of interest may be an entire country or a morespecific geographic region (e.g., a designated market area (DMA) orother local market area). DMAs may range in size from several millionhouseholds for a large metropolitan area down to a few thousandhouseholds in a rural market area.

Typically, national ratings are generated based on audience measurementdata collected via people meters installed in statistically selectedpanelist households. The people meters monitor the exposure of paneliststo media and automatically report such data to an AME for subsequentanalysis and processing. In addition, some AMEs rely on additionalpanelists in the smaller local market areas to record their mediaconsumption behavior in paper diaries over specified periods of time andthen mail the completed diaries to the AME for subsequent analysis andprocessing. While paper diaries provide a relatively inexpensive methodto increase the audience measurement sample size, what panelists recordin the paper diaries may not always be accurate, thereby introducingpotential biases in the data. Furthermore, diary samples often favorhighly rated broadcast stations while neglecting smaller broadcast orcable networks such that the collected panel data may not be fullyrepresentative for reliable analysis.

As technology has advanced, AMEs have turned to tuning data collected,measured, and/or reported from RPD devices as an alternate source ofdata that may be used to generate ratings for media and/or otheraudience measurement metrics. As used herein, an RPD device refers toany type of media device (e.g., a STB or other similar device) that iscapable of accessing media from a media provider and reporting tuningdata regarding the media accessed back to the media provider. Suchtuning data is referred to herein as RPD tuning data or simply RPD.Using RPD tuning data for audience metrics has the advantage that it isrelatively inexpensive to obtain. For example, RPD tuning data may beobtained substantially automatically based on software installed onprocessors associated with the RPD devices reporting the RPD tuning datavia any suitable network (e.g., the Internet). Not only is RPD tuningdata relatively inexpensive to collect based on modern computertechnology that makes the reporting of such RPD tuning data possible,RPD tuning data is also advantageous in that it may be collected frommuch larger quantities of households than possible through traditionalaudience measurement panels. For example, RPD tuning data may becollected from virtually every household that includes an RPD devicebecause the reporting of RPD tuning data is often set as the defaultoption for such devices at the time of manufacture.

While RPD tuning data provides these advantages, there are challengeswith relying exclusively, or even partially, on RPD tuning data forpurposes of audience measurement. Even where a household has an RPDdevice to report tuning data (e.g., the household subscribes to a mediacontent provider), the same household may have other media devices thatare not capable of reporting RPD tuning data. Such devices are referredto herein as non-RPD devices. As a result, RPD tuning data collected insuch households may not account for media exposure of audience membersin non-RPD devices. Therefore, in some examples RPD tuning data reportedfor a household may not account for all media exposure in the householdand, thus, may be biased or illogical. Furthermore, the STBs thatproduce RPD are often not turned off reliably. As such, when atelevision is turned off, the STB may still be on and will report RPDduring the time the television was turned off. Additionally, when someSTBs undergo software updates, they provide RPD that indicates allstations as being active. Thus, while RPD can be collected for a widearray of people, it may be missing tuning data or providing tuning datathat was never actually watched.

Example methods, apparatus, and articles of manufacture disclosed hereinovercome at least some of the limitations associated with determiningmedia ratings in local markets based on local RPD tuning data by usingpanel tuning data collected in the surrounding region to the localmarket to calibrate the RPD tuning data to correct for biases in the RPDtuning data.

Examples disclosed herein calibrate RPD tuning data by classifyingviewing periods associated with one or more segments of return path datareceived from a set top box into one or more tuning classifications(e.g., live tuning, playback tuning, etc.) based on the one or moresegments of the return path data. In examples disclosed herein a viewingperiod can cover multiple segments of tuning data in the return pathdata. For example, a viewing period may cover one minute and, as such,there may be 1440 viewing periods for a given day, representative of the1440 total minutes in a day. Alternatively, the viewing period may coverany period of time (partial minutes, multiple minutes, hours, days,weeks, etc.). Segments of tuning data may represent any segment of time(e.g., seconds, minutes, hours, days, etc.) for which RPD tuning data isto be calibrated (e.g., corresponding to the granularity of tuning datato be calibrated).

In some examples disclosed herein, a total reported tuning duration fora viewing period may be calculated when the viewing period is classifiedas at least one of live or playback tuning. For example, the totalreported tuning duration may be calculated by combining tuning segmentsfor a viewing period reported by a STB. In some examples, the segmentsof tuning data have respective reported tuning durations that contributeto the total reported tuning duration for a viewing period. For example,each segment of a viewing period may have a corresponding reportedtuning duration that is combined to contribute to the total reportedtuning duration for the viewing period. In some examples, the segmentsmay combine to generate a total reported tuning duration that exceedsthe viewing period, which is indicative of error(s) in the RPD. Examplesdisclosed herein calibrate the RPD tuning data to correct for sucherrors. Thus, the total reported tuning duration may be compared to aduration threshold (e.g., a value greater than the viewing period) todetermine whether segments of return path data associated with a viewingperiod are valid. In some examples, calibration is also based on anumber of tuning sources (e.g., channel numbers, station codes, etc.)tuned by a set top box during a viewing period as determined based onthe segments of return path data associated with the viewing period.

In some examples disclosed herein, the segments of return path dataassociated with the viewing period are identified as valid when thetotal reported tuning duration satisfies the duration threshold and thenumber of tuning sources tuned during the viewing period satisfies atuning source threshold. The tuning source threshold may be determinedbased on a particular media provider, for example.

Once the RPD tuning data is identified as valid, it may be utilized torectify missing tuning data associated with a second viewing period. Forexample, when the segments of the return path data associated with afirst viewing period are determined to be valid, the missing return pathdata associated with the second viewing period may be replaced with thetuning data included in the segments of return path data associated withthe first viewing period. For example, segments for a first viewingperiod may be identified as valid and may be associated with program A.As such, the missing return path data may be rectified to be associatedwith program A, for example. However, as detailed below, other rules maybe applied to rectify and/or calibrate RPD.

FIG. 1 is an example environment 100 including an example mediacalibration module 122 to calibrate return path data for audiencemeasurement in accordance with the teachings of this disclosure. In theillustrated example, an example media provider 102 provides media tosubscribers and collects RPD tuning data indicative of the subscribersaccessing the media. The media provider 102 may provide the RPD tuningdata to an example audience measurement entity (AME) 104 to enable theAME 104 to generate audience measurement metrics. In some examples, themedia provider 102 and the AME 104 communicate via an example network106 such as, for example, the Internet.

As shown in FIG. 1 , the example environment 100 includes an examplenon-panelist household 108, and an example panelist household 110. Thepanelist household 110 represents households that have members that haveenrolled as panelists with the AME 104, whereas non-panelist household108 represents households that are not enlisted with the AME 104. Theremay be any number of panelist households 110 and non-panelist households108 in the environment 100. In some examples, panelists correspond to astatistically selected subset of all potential audience members that isrepresentative of a population of interest. In some such panel-basedmonitoring systems, the panelists agree to provide detailed demographicinformation about themselves. In this manner, detailed exposure metricsare generated based on collected media exposure data and associated userdemographics, which can then be statistically extrapolated to an entirepopulation of interest (e.g., a local market, a national market, ademographic segment, etc.).

In the illustrated example, the non-panelist household 108 includes anexample RPD device 112 and an example non-RPD device 114. The panelisthousehold 110 differs in that the panelist household 110 includes anexample RPD device 116, an example non-RPD device 118, and an examplemeter 120. However, the non-panelist household 108 can include anynumber of RPD devices 112 and/or non-RPD devices 114. Likewise, thepanelist household 110 can include any number of RPD devices 116 and/ornon-RPD devices 118 and/or meters 120. As described above, an RPDdevice, as used herein, is any type of media device capable of accessingmedia from a media provider 102 and reporting RPD tuning data back tothe media provider. By contrast, a non-RPD device, as used herein,refers to any type of media device that is capable of accessing and/orplaying media from a media provider 102 but that does not have thecapability to report RPD tuning data back to the media provider 102, ordoes not have such capabilities enabled.

In the illustrated example of FIG. 1 , the non-panelist household 108and the panelist household 110 include the RPD devices 112, 116 becausethe households are subscribers to the media provider 102. In someexamples, the RPD devices 112, 116 are provided by the media provider102 when the households initially become subscribers to enable access tomedia generated by media provider 102. As shown in the illustratedexample, the RPD devices 112, 116 may access media from the mediaprovider 102 and report RPD tuning data to the media provider 102 viathe network 106.

As shown in the illustrated example, the households 108, 110 may includenon-RPD devices 114, 118 in addition to the RPD devices 112, 116.However, a household may have any number of RPD devices and/or non-RPDdevices, but does not have to have any RPD devices (i.e., capable ofreporting RPD tuning data that is available to the AME 104) or non-RPDdevices.

In the illustrated example, the RPD devices 112, 116 may be standalonedevices (e.g., STBs, cable modems, embedded multimedia adapters (EMTAs))that connect to separate media presentation devices, such as, televisionsets, radios, smartphones, tablets, computers, or any other devicecapable of playing the media accessed by the RPD devices 112, 116. Insome examples, the RPD devices 112, 116 may be integrated with acorresponding media presentation device capable of playing the mediaaccessed by the RPD device (e.g., a smart television). Similarly, thenon-RPD devices 114, 118 may be integrated media presentations devicesor standalone devices (e.g., STBs) that connect to separate mediapresentation devices.

As described herein, RPD devices are capable of reporting RPD tuningdata to a media provider 102, but non-RPD devices do not. Thus, in theillustrated example, RPD tuning data collected by the media provider 102would be limited to media accessed via the RPD devices 112, 116. Suchdata is incomplete as it does not represent the complete exposure tomedia by all households. For example, the RPD tuning data would notindicate any media exposure by audience members using only non-RPDdevices 114, 118. Further, while the RPD tuning data would convey somemedia to which audience members in the households 108, 110 were exposed,any media accessed via the non-RPD devices 114, 118 is not accounted forin the reported RPD tuning data.

While the RPD tuning data collected from the RPD devices 112, 116 isinsufficient to fully account for all media accessed in any of thehouseholds, the AME 104 is at least able to fully account for much, andpossibly all, of media accessed at the panelist household 110. This ispossible because the panelist household 110 is provided with themetering device 120 to track and/or monitor media played in thehouseholds 110 and report such to the AME 104 (e.g., via the network106). In some examples, the metering device 120 also tracks and reportswho is being exposed to the media being played so that the mediaexposure can be associated with particular individuals and theirassociated demographics previously collected when the household membersenrolled as panelists. While a single metering device 120 is shown inthe panelist household 110 to monitor both the RPD device 116 and thenon-RPD device 118, in some examples, a separate metering device 120 maybe associated with each device to independently track and report mediaaccessed by each device to the AME 104.

In the illustrated example of FIG. 1 , the AME 104 includes the exampleRPD calibration module 122 to calibrate RPD tuning data used to completeratings data as described more fully below. More particularly, the RPDcalibration module 122 uses panel tuning data included in audiencemeasurement data collected from panelist households (e.g., from themetering device 120 of the panelist household 110) to calibrate RPD. Insome examples, the RPD calibration module 122 determines whether RPD iscomplete and logical (e.g., validating tuning segments of a viewingperiod) and rectifies incomplete tuning data. In some examples, the RPDcalibration module 122 identifies tuning across viewing periods missingtuning data, with such tuning being identified based on adjacent tuningdata as reported in the RPD. For example, RPD received from the RPDdevice 116 may be missing multiple segments of tuning data for a givenviewing period. As such, the RPD calibration module 122 may identifysegments of tuning data that occur prior to and subsequent the missingsegments, and modify (e.g., bridge the gap) the missing segments basedon bridging rules. The bridging rules are discussed in more detail belowin connection with FIG. 2 . In some examples, the RPD calibration module122 generates a grid of segments for viewing periods for each STBincluded in the RPD. For example, the RPD calibration module may receiveRPD from each RPD device 112, 116 and generate a grid of segments foreach viewing period. In some examples, the RPD calibration module 122then evaluates the segments to determine if the RPD is valid (e.g.,usable) for reporting for a given day. Examples disclosed hereincalibrate RPD without the need or expense of employing paper diaries orhaving a large sample size of panelists specifically located within thelocal market area.

FIG. 2 is an example implementation of the example RPD calibrationmodule 122 of FIG. 1 . The example RPD calibration module 122 includesan example communications interface 202, an example RPD classifier 204,an example RPD validator 206, an example RPD rectifier 208, an examplepanel tuning data database 210, and an example RPD tuning data database212.

The example RPD calibration module 122 is provided with the examplecommunications interface 202 to communicate with the metering device 120installed in the panelist household 110. That is, the metering device120 may report audience measurement data to the AME 104 that is receivedby the communications interface 202. The communications interface 202may receive audience measurement data from other panelist households notrepresented in the illustrated example. The collected audiencemeasurement data includes panel tuning data, which may be stored in thepanel tuning data database 210. The panel tuning data may include anindication of the media accessed via the associated media devices (e.g.,the RPD device 116 or the non-RPD device 118). In the illustratedexample, the panel tuning data includes an identifier of the particularmedia device used to access the media and/or an indication of whetherthe media device is capable of reporting RPD tuning data (i.e., whetherthe media device is an RPD device). In some examples, the media accessedby the media devices may be uniquely identified by the panel tuningdata. In some examples, the panel tuning data may identify a particularsource of media (e.g., a station ID) from which the particular media maybe identified based on an associated timestamp included in the paneltuning data.

In the illustrated example, the communications interface 202 of the RPDcalibration module 122 receives RPD tuning data from the media provider102. The media provider 102 collects the RPD tuning data reported fromRPD devices (e.g., the RPD devices 112, 116) accessing media provided bythe media provider 102. In some examples, the communications interface202 may receive the RPD tuning data directly from the RPD devices 112,116 independent of communications between the AME 104 and the mediaprovider 102. The RPD tuning data may be stored in the RPD tuning datadatabase 212. Similar to the panel tuning data, the RPD tuning dataincludes a media identifier (e.g., a unique identifier, a station IDwith an associated timestamp, etc.) to identify the media accessed bythe RPD devices.

To classify the RPD tuning data, the RPD classifier 204 receives the RPDtuning data for the non-panelist household 108 and the panelisthousehold 110. In some examples, the RPD classifier 204 receives the RPDtuning data from the communications interface 202. In some examples, theRPD classifier 204 may retrieve the RPD tuning data from the RPD tuningdata database 212. Once the RPD tuning data has been received, the RPDclassifier 204 classifies each viewing period corresponding to the RPDtuning data. For example, the RPD classifier 204 may receive RPD tuningdata indicative of one day of tuning data. As such, the RPD classifier204 may generate a grid indicative of the 1440 minutes in a day. Eachsection of the grid represents a viewing period (e.g., a viewing periodcorresponds to a minute in this example, but viewing periods may haveother durations in other examples.). The example RPD classifier 204classifies each viewing period in the grid. For example, the RPDclassifier 204 may classify each viewing period as either live tuning,playback tuning, OFF, stand-by and/or gap tuning. To classify eachviewing period, the RPD classifier 204 may classify each segment of RPDof the viewing period based on descriptive data included with the RPDsegment, and compare the classification to a threshold. For example, theRPD classifier 204 may identify that 29 segments (e.g., seconds) of theviewing period correspond to live tuning based on descriptive dataincluded with the RPD segments, while the remaining segments correspondto playback tuning based on descriptive data included with the RPDsegments. In such an example, the RPD classifier 204 classifies thatviewing period as playback tuning based on the majority of the segmentscorresponding to playback tuning. In some examples, the RPD classifier204 may classify the viewing period as both live tuning and playbacktuning. In some examples, the RPD classifier 204 may classify a viewingperiod as OFF when the RPD tuning data is indicative of the STB beingturned off. The example RPD classifier 204 may classify a viewing periodas gap tuning if there is missing or illogical tuning data, or if thereis not a sufficient amount of RPD tuning data to classify the viewingperiod. In some examples, the RPD classifier 204 may utilize heartbeatdata (e.g., information indicative of a STB functioning properly) toclassify a viewing period. For example, if heartbeat data is expected tobe generated for a specific viewing period or for a specific period oftime, the RPD classifier 204 may classify that viewing period and/orviewing periods as gap tuning if the heartbeat data is missing. As anexample, for a certain type of STB, heartbeat data is expected at oraround 12:00 AM, and a majority of STBs of this type are known togenerate heartbeat data between 11:45 PM and 12:15 AM. As such, if theRPD classifier 204 identifies heartbeat data for a certain STB at 2:00AM, the RPD classifier 204 may identify the viewing periods between12:15 AM and 2:00 AM as gap tuning. In another example, heartbeat datamay be expected every eight hours for a given type of STB, with amajority of STBs of that type generating heartbeat data every seven anda half to eight and a half hours. In such an example, the RPD classifier204 may identify heartbeat data for such STB at 1:00 AM and heartbeatdata for the same STB at 10:00 AM. In that example, the RPD classifier204 may classify the viewing periods between 9:30 AM and 10:00 AM as gaptuning.

Once the RPD classifier 204 has classified the viewing periods ofinterest, the RPD classifier 204 further classifies the viewing periodsthat were classified as live tuning and/or playback tuning. For example,for each viewing period that was classified as live tuning and/orplayback tuning, the RPD classifier 204 calculates a total reportedtuning duration for the given viewing period and compares it to athreshold. For example, the RPD classifier 204 may calculate a totalreported tuning duration by accumulating individual reported tuningdurations for respective RPD segments included in a viewing period, suchas a given minute of a day. The RPD classifier 204 may calculate thetotal reported tuning duration based on start/end times for theclassified viewing period. As an example, based on descriptive data ofthe RPD, the RPD classifier 204 may identify live tuning to station XYZfrom 1:01:05 PM to 1:03:15 PM, playback tuning to station UVW from1:01:01 PM to 1:15:08 PM, and live tuning to station RST from 1:02:33 PMto 1:45:00 PM. In such an example, the RPD classifier 204 will calculatea total reported tuning duration of 147 seconds for the viewing periodrepresentative of 1:02:00 PM (corresponding to 60 seconds for stationXYZ, 60 seconds for station UVW, and 27 seconds for station RST). Inthis example, the viewing period represents a minute, so the thresholdmay be set at 65 seconds. As such, the RPD classifier 204 may classifythe viewing period for 1:02:00 PM as an overlapping minute because thetotal reported tuning duration of 147 seconds exceeds the threshold of65 seconds.

For the viewing periods classified as overlapping minutes, the RPDclassifier 204 calculates a number of tuning sources (e.g., channelnumbers, station codes, etc.) that account for at least a thresholdportion of the viewing period. For example, the RPD classifier 204 mayclassify a viewing period as conflicted tuning if the number of tuningsources that account for at least 31 seconds of the viewing periodexceeds a threshold (e.g., 2 tuning sources, 4 tuning sources, etc.).The RPD classifier 204 continues to classify the viewing periods untilall of the viewing periods have been classified.

In the foregoing example, reported tuning duration was determined inunits of seconds. However, in other examples, the reported tuningduration may be determined in other units, such as inportions/fractions, etc. of the viewing period.

Once the RPD tuning data has been classified, the RPD validator 206validates the classified RPD tuning data. To validate the RPD tuningdata, the RPD validator 206 generates activity validation data based onthe RPD tuning data from the panelist household 110. For example, theRPD validator 206 may generate the activity validation data based on theinformation received from the RPD device 116, the non-RPD device 118,and the meter 118. In some examples, the RPD validator 206 may generatethe activity validation data based on the panel tuning data stored inthe panel tuning data database 212. To generate the activity validationdata, the RPD validator 206 generates a grid corresponding to theviewing periods for the reported RPD tuning data. The RPD validator 206may further include identifiers (device identifiers, media identifiers,etc.) for each of the viewing periods in the grid. In some examples, theRPD validator 206 generates the activity validation data prior to theRPD classifier 204 classifying the RPD tuning data. Once the activityvalidation data has been generated, the RPD validator 206 processes theclassified RPD tuning data from the RPD classifier 204.

In the illustrated example, to validate the classified RDP tuning data,the RPD validator 206 determines if the RPD tuning data is complete. Forexample, the RPD validator 206 may determine 1) if there is a thresholdamount of heartbeat data, 2) whether specific information was collectedfrom the RPD tuning data (e.g., the STB was tuned to a certain stationand not off during a time period of interest), and/or 3) when thespecific information was collected. In some examples, the RPD validator206 determines if RPD tuning segment(s) for each viewing periodhas(have) start and end times that are consistent. For example, when theRPD validator 206 identifies playback tuning (e.g., time-shiftedtuning), the RPD validator 206 validates that there are two sets ofstart and end times, one corresponding to the broadcast duration and theother corresponding to the playback duration. The RPD validator 206further determines that the playback tuning is valid by determining thatthe broadcast duration is equal to the playback duration. If the RPDvalidator 206 identifies any discrepancies based on the RPD informationnot satisfying the above criteria, the RPD validator 206 may edit theRPD tuning data based on the activity validation data or may remove theRPD tuning data from further processing.

In some examples, to edit the RPD tuning data, the RPD validator 206identifies any non-residential STBs (e.g., commercial accounts, multipledwelling units, etc.) and removes the RPD tuning data from furtherprocessing. If information is not available to identify non-residentialstatus, the RPD validator 206 identifies accounts that are associatedwith a large number of STBs, for example 20 or more, and removes the RPDinformation from further processing. Once the non-residential RPD tuningdata has been removed, the RPD validator 206 identifies incomplete RPDtuning data that may need to be validated and/or edited. For example,the RPD validator 206 verifies that each segment(s) of RPD tuning dataassociated with that viewing period is associated with at least onetuning source. If multiple segments of RPD tuning data associated withviewing periods are not associated with a tuning source, the RPDvalidator 206 compares the total tuning duration (X) to the totalduration of tuning that cannot be mapped (Y). The RPD validator 206determines if the ratio of X to Y (X/Y) exceeds a certain threshold. Ifthe RPD validator 206 determines that X/Y exceeds the threshold, the RPDtuning data is considered unusable and is removed from furtherprocessing.

After the RPD tuning data has been classified and validated, the RPDrectifier 208 identifies tuning sessions (e.g., multiple viewing periodswith similar tuning classifications) for the RPD tuning data. The RPDrectifier 208 may identify the tuning sessions by looking for at leastone of a change in station or tuning state (e.g. channel change, STBturned off, change from live tuning to playback tuning) in the RPDtuning data, or viewing periods identified as gap tuning. The RPDrectifier 208 then identifies each viewing period in the tuning sessionand identifies viewing periods that are missing tuning data (e.g.,classified as gap tuning).

The RPD rectifier 208 then modifies the RPD tuning segments for theviewing periods with missing tuning data based on bridging rules. Insome examples, the bridging rules include: 1) if the RPD tuning segmentis at the start of the tuning session, the segment is rectified to matchthe tuning data of the following segment; 2) if the RPD tuning segmentis at the end of the tuning session, the segment is rectified to matchthe tuning data of the preceding segment; 3) if segments on either sideof the RPD tuning segment classified as gap tuning have the same tuningdata, the segment classified as gap tuning is rectified to match thattuning data. If segments on either side of the segment classified as gaptuning have different tuning data, the segment classified as gap tuningis assigned tuning data based on one of the following methods: 1) applya hierarchy whereby, among tuning data on either side of the segmentclassified as gap tuning, preference is based on: i) live tuning overplayback tuning (or vice versa), ii) live tuning or playback tuning overstand-by, etc.; 2) the viewing period of adjacent tuning data is used;or 3) a portion of a segment will be randomly selected. For instanceswhen the portion of the segment is randomly selected, the RPD rectifier208 rectifies the segment to include the tuning data of the segmentprior to the portion of the segment classified as gap tuning up to theportion of the segment. The RPD rectifier 208 rectifies the remainingportion of the segment to include tuning data from the segment followingthe end of the portion of the segment.

Once the RPD rectifier 208 has rectified the RPD tuning data, the RPDrectifier 208 determines if the RPD tuning data for a household (e.g.,non-panelist household 108) is usable (e.g., in-tab) for a given day.For example, the RPD rectifier 208 determines that all active STBs forthe household (e.g., non-panelist household 108) have the same zip codeand/or headend. The RPD rectifier 208 then determines that the remainingsegments classified as gap tuning does not exceed a gap tuning threshold(e.g., 10% or some other value). The RPD rectifier 208 then determinesif the segments associated with viewing periods classified as conflictedtuning does not exceed a conflicted tuning threshold (e.g., 3%).Further, the RPD rectifier 208 may determine that the number of segmentsassociated with viewing periods classified as live tuning does notexceed a live tuning threshold (e.g., 300). In some examples, if the RPDtuning data does not include any missing or illogical data (e.g., theRPD rectifier 208 was able to rectify all of the RPD tuning data), thenthe RPD rectifier 208 may not determine if any segments exceed the gaptuning threshold.

While an example manner of implementing the RPD calibration module 122of FIG. 1 is illustrated in FIG. 2 , one or more of the elements,processes and/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example communications interface 202, the example RPDclassifier 204, the example RPD validator 206, the example RPD rectifier208, and/or, more generally, the example RPD calibration module 122 ofFIG. 1 may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example communications interface 202, the example RPDclassifier 204, the example RPD validator 206, the example RPD rectifier208, and/or, more generally, the example RPD calibration module 122could be implemented by one or more analog or digital circuit(s), logiccircuits, programmable processor(s), programmable controller(s),graphics processing unit(s) (GPU(s)), digital signal processor(s)(DSP(s)), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)). When reading any of the apparatus or system claimsof this patent to cover a purely software and/or firmwareimplementation, at least one of the example communications interface202, the example RPD classifier 204, the example RPD validator 206, theexample RPD rectifier 208, and/or, more generally, the example RPDcalibration module 122 is/are hereby expressly defined to include anon-transitory computer readable storage device or storage disk such asa memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. including the software and/or firmware. Further still, theexample RPD calibration module 122 of FIG. 1 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 2 , and/or may include more than one of any or allof the illustrated elements, processes and devices. As used herein, thephrase “in communication,” including variations thereof, encompassesdirect communication and/or indirect communication through one or moreintermediary components, and does not require direct physical (e.g.,wired) communication and/or constant communication, but ratheradditionally includes selective communication at periodic intervals,scheduled intervals, aperiodic intervals, and/or one-time events.

Flowcharts representative of example hardware logic or machine readableinstructions for implementing the RPD calibration module 122 of FIG. 1are shown in FIGS. 3-6 . The machine readable instructions may be aprogram or portion of a program for execution by a processor such as theprocessor 712 shown in the example processor platform 700 discussedbelow in connection with FIG. 7 . The program may be embodied insoftware stored on a non-transitory computer readable storage mediumsuch as a CD-ROM, a floppy disk, a hard drive, a DVD, a Blu-ray disk, ora memory associated with the processor 712, but the entire programand/or parts thereof could alternatively be executed by a device otherthan the processor 712 and/or embodied in firmware or dedicatedhardware. Further, although the example program is described withreference to the flowcharts illustrated in FIGS. 3-6 , many othermethods of implementing the example RPD calibration module 122 mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined. Additionally or alternatively, any or all ofthe blocks may be implemented by one or more hardware circuits (e.g.,discrete and/or integrated analog and/or digital circuitry, an FPGA, anASIC, a comparator, an operational-amplifier (op-amp), a logic circuit,etc.) structured to perform the corresponding operation withoutexecuting software or firmware.

As mentioned above, the example processes of FIGS. 3-6 may beimplemented using executable instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, and(6) B with C.

FIG. 3 is a flowchart representative of example machine readableinstructions that may be executed to implement the RPD calibrationmodule 122 of FIGS. 1 and/or 2 . The program of FIG. 3 begins byclassifying RPD information (block 302). For example, the RPD classifier204 may receive RPD tuning data for the non-panelist household 108 andthe panelist household 110. The RPD validator 206 then validates the RPDinformation (block 304). The RPD validator 204 then determines if theRPD information is valid (block 306). If the RPD information is notvalid, the process ends. If the RPD information is valid, the RPDrectifier 208 rectifies the validated RPD information (block 308). TheRPD rectifier 208 then determines if the RPD information is indicativeof a suitable household (block 310). If the RPD information is notindicative of a suitable household, the RPD rectifier 208 removes theRPD information (block 312). If the RPD information is indicative of asuitable household, the process ends.

FIG. 4 is a flowchart representative of example machine readableinstructions that may be executed to implement the RPD classifier 204 toclassify RPD information and/or to perform the processing at block 302of FIG. 3 . The process 302 of FIG. 4 begins when the RPD classifier 204receives RPD tuning data form panelist households and non-panelisthouseholds (block 402). The RPD classifier 204 then classifies eachviewing period corresponding to the RPD tuning data (block 404). Foreach viewing period classified as live or playback tuning (block 406),the RPD classifier 204 calculates a total reported tuning duration forthe given viewing period (block 408). The RPD classifier 204 thendetermines if the reported tuning duration for the given viewing periodexceeds a threshold (block 410). If the reported tuning duration doesnot exceed the threshold, the RPD classifier 204 determines if allviewing periods have been evaluated (block 418). If all viewing periodshave been evaluated, the process returns to block 302. If all viewingperiods have not been evaluated, the process returns to block 406 toselect another viewing period to classify.

However, if the reported tuning duration exceeds the threshold, the RPDclassifier 204 classifies the given viewing period as an overlappingviewing period (block 412). The RPD classifier 204 then determines ifthe tuning source threshold has been exceeded for the given viewingperiod (block 414). If the tuning source threshold has not beenexceeded, the process proceeds to block 418 to determine if all viewingperiods have been evaluated. However, if the tuning source threshold hasbeen exceeded, the RPD classifier 204 classifies the given viewingperiod as conflicted tuning (block 416). The RPD classifier 204 thendetermines if all viewing periods have been evaluated (block 418). Ifall viewing periods have been evaluated, the process returns to block302. If all viewing periods have not been evaluated, the process returnsto block 406 to select another viewing period to classify. The processthen returns to block 302.

FIG. 5 is a flowchart representative of example machine readableinstructions that may be executed to implement the RPD validator 206 tovalidate RPD information and/or to perform the processing at block 304of FIG. 3 . The process 304 of FIG. 5 begins when the RPD validator 206generates activity validation data based on the RPD tuning data from thepanelist households (block 502). The RPD validator 206 then determinesif the RPD tuning data from the non-panelist households is valid (block504). If the RPD tuning data from the non-panelist households is valid,the process proceeds to block 508 to adjust tuning data based on theactivity validation data. If the RPD tuning data is not valid, the RPDvalidator 206 determines if the RPD tuning data can be adjusted (block506). If the RPD tuning data cannot be adjusted, the RPD validator 206signals that the RPD tuning data cannot be adjusted (block 510). If theRPD tuning data can be adjusted, the RPD validator 206 adjusts thetuning data based on the activity validation data (block 508). Theprocess then returns to block 304.

FIG. 6 is a flowchart representative of example machine readableinstructions that may be executed to implement the RPD rectifier 208 torectify RPD information and/or to perform the processing at block 308 ofFIG. 3 . The process 308 of FIG. 6 begins when the RPD rectifier 208identifies viewing periods for the validated RPD tuning data (block602). The RPD rectifier 208 then identifies segments of the viewingperiod missing tuning data (block 604). The RPD rectifier 208 thenmodifies segments missing tuning data based on bridging rules (block606). For example, the RPD rectifier 208 modifies the segments based onthe bridging rules detailed above in connection with FIG. 2 . Theprocess then ends.

FIG. 7 is a block diagram of an example processor platform 700structured to execute the instructions of FIGS. 3-6 to implement the RPDcalibration module 122 of FIG. 1 . The processor platform 700 can be,for example, a server, a personal computer, a workstation, aself-learning machine (e.g., a neural network), a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad™), a personaldigital assistant (PDA), an Internet appliance, a DVD player, a CDplayer, a digital video recorder, a Blu-ray player, a gaming console, apersonal video recorder, a set top box, a headset or other wearabledevice, or any other type of computing device.

The processor platform 700 of the illustrated example includes aprocessor 712. The processor 712 of the illustrated example is hardware.For example, the processor 712 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors, GPUs, DSPs, orcontrollers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor implements the example RPD classifier 204,the example RPD validator 206, the example RPD rectifier 208, and/or,more generally, the example RPD calibration module 122.

The processor 712 of the illustrated example includes a local memory 713(e.g., a cache). The processor 712 of the illustrated example is incommunication with a main memory including a volatile memory 714 and anon-volatile memory 716 via a bus 718. The volatile memory 714 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory(RDRAM®) and/or any other type of random access memory device. Thenon-volatile memory 716 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 714, 716is controlled by a memory controller.

The processor platform 700 of the illustrated example also includes aninterface circuit 720. The interface circuit 720 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 722 are connectedto the interface circuit 720. The input device(s) 722 permit(s) a userto enter data and/or commands into the processor 712. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a trackpad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 724 are also connected to the interfacecircuit 720 of the illustrated example. The output devices 724 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 720 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 720 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 726. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc. In the illustrated example, the interface circuit 720implements the example communication interface 202 at the example RPDcalibration module 122.

The processor platform 700 of the illustrated example also includes oneor more mass storage devices 728 for storing software and/or data.Examples of such mass storage devices 728 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives. In some examples, the mass storage device 728 implementsthe example panel tuning data database 210, and/or the example RPDtuning data database 212 of the example RPD calibration module 122.Additionally or alternatively, in some examples, the memory 714implements the example panel tuning data database 210, and/or theexample RPD tuning data database 212 of the example RPD calibrationmodule 122

The machine executable instructions 732 of FIGS. 3-6 may be stored inthe mass storage device 728, in the volatile memory 714, in thenon-volatile memory 716, and/or on a removable non-transitory computerreadable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that provideimproved functionality for a processor or other computer deviceanalyzing RPD tuning data collected from households. Such RPD tuningdata is calibrated such that the RPD tuning data may improve theaccuracy of corresponding results and/or improve the subsequentprocessing of the RPD tuning data.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus comprising: communications interfacecircuitry to receive, via a network, first and second return path datafrom a plurality of households that include set top boxes, the pluralityof households associated with a geographic region having a populationsuch that the households number in at least the thousands; memory tostore the first and second return path data; instructions; and processorcircuitry to execute the instructions to: classify first viewingperiods, associated with respective ones of the households, into atleast one of a live tuning classification or a playback tuningclassification based on one or more segments of the first return pathdata, the first return path data corresponding to the first viewingperiods; and classify second viewing periods, associated with respectiveones of the households, into a gap tuning classification based on one ormore segments of the second return path data, the second return pathdata corresponding to the second viewing periods, the gap tuningclassification to indicate the second return path data is associatedwith missing tuning data; select at least one of a plurality ofavailable methods to rectify the missing tuning data, the at least oneof the plurality of available methods to be selected based on ahierarchy of different tuning classifications, the different tuningclassifications including the live tuning classification and theplayback tuning classification; and rectify the second return path dataassociated with the missing tuning data, using the at least one of theplurality of available methods, based on valid tuning data included inthe first return path data, rectification of the second return path datato improve operation of a computer determining media ratings based onthe second return path data by correcting for bias in the second returnpath data.
 2. The apparatus of claim 1, wherein the processor circuitryis to replace the missing tuning data associated with the second viewingperiods with the tuning data included in one or more segments of returnpath data associated with the first viewing periods to rectify themissing tuning data.
 3. An apparatus comprising: communicationsinterface circuitry to receive, via a network, first and second returnpath data from a plurality of households that include set top boxes, theplurality of households associated with a geographic region having apopulation such that the households number in at least the thousands;instructions; and processor circuitry to execute the instructions to:classify first viewing periods, associated with respective ones of thehouseholds, into at least one of a live tuning classification or aplayback tuning classification based on one or more segments of thefirst return path data, the first return path data corresponding to thefirst viewing period; classify second viewing periods, associated withrespective ones of the households, into a gap tuning classificationbased on one or more segments of the second return path data, the secondreturn path data corresponding to the second viewing periods, the gaptuning classification to indicate the second return path data isassociated with missing tuning data; determine whether a segment oftuning data covered by the first viewing periods has a start time and anend time that are consistent relative to a broadcast start time and abroadcast end time; determine the first return path data includes validtuning data based on the start time and the end time being consistentrelative to the broadcast start time and the broadcast end time; andrectify the second return path data associated with the missing tuningdata based on the valid tuning data included in the first return pathdata, rectification of the second return path data to improve operationof a computer determining media ratings based on the second return pathdata by correcting for bias in the second return path data.
 4. Theapparatus of claim 3, wherein the first return path data is obtainedfrom a non-panelist household, and the processor circuitry is to:generate activity validation data based on third return path dataobtained from a panelist household; and when the start time and the endtime of the segment of tuning data for the first return path data arenot consistent, adjust the first return path data to include the validtuning data based on the activity validation data.
 5. The apparatus ofclaim 1, wherein a first one of the first viewing periods, associatedwith a first one of the households, covers multiple segments of tuningdata, and the processor circuitry is to: determine whether ones of themultiple segments of tuning data are associated with at least one tuningsource; when more than one of the multiple segments of tuning data isnot associated with at least one tuning source, compare a total tuningduration of the first one of the first viewing periods to mapped tuningduration of the first one of the first viewing periods, the mappedtuning duration corresponding to a duration of the ones of the multiplesegments of tuning data associated with at least one tuning source; anddetermine the first return path data includes the valid tuning databased on the comparison of the total tuning duration to the mappedtuning duration.
 6. A non-transitory computer readable medium comprisinginstructions that, when executed, cause a machine to at least: receive,via a network, first and second return path data from a plurality ofhouseholds that include set top boxes, the plurality of householdsassociated with a geographic region having a population such that thehouseholds number in at least the thousands; classify first viewingperiods, associated with respective ones of the households, into atleast one of a live tuning classification or a playback tuningclassification based on one or more segments of the first return pathdata, the first return path data corresponding to the first viewingperiods; classify second viewing periods, associated with respectiveones of the households, into a gap tuning classification based on one ormore segments of the second return path data, the second return pathdata corresponding to the second viewing periods, the gap tuningclassification to indicate the second return path data is associatedwith missing tuning data; select at least one of a plurality ofavailable methods to rectify the missing tuning data, the at least oneof the plurality of available methods to be selected based on ahierarchy of different tuning classifications, the different tuningclassifications including the live tuning classification and theplayback tuning classification; and rectify the second return path dataassociated with the missing tuning data, using the at least one of theplurality of available methods, based on valid tuning data included inthe first return path data, rectification of the second return path datato improve operation of a computer determining media ratings based onthe second return path data by correcting for bias in the second returnpath data.
 7. The non-transitory computer readable medium of claim 6,wherein the instructions cause the machine to replace the missing tuningdata associated with the second viewing periods with the tuning dataincluded in one or more segments of return path data associated with thefirst viewing periods to rectify the missing tuning data.
 8. Anon-transitory computer readable medium comprising instructions that,when executed, cause a machine to at least: receive, via a network,first and second return path data from a plurality of households thatinclude set top boxes, the plurality of households associated with ageographic region having a population such that the households number inat least the thousands; classify first viewing periods, associated withrespective ones of the households, into at least one of a live tuningclassification or a playback tuning classification based on one or moresegments of the first return path data, the first return path datacorresponding to the first viewing period; classify second viewingperiods, associated with respective ones of the households, into a gaptuning classification based on one or more segments of the second returnpath data, the second return path data corresponding to the secondviewing periods, the gap tuning classification to indicate the secondreturn path data is associated with missing tuning data; determinewhether a segment of tuning data covered by the first viewing periodshas a start time and an end time that are consistent relative to abroadcast start time and a broadcast end time; determine the firstreturn path data includes valid tuning data based on the start time andthe end time being consistent relative to the broadcast start time andthe broadcast end time; and rectify the second return path dataassociated with the missing tuning data based on the valid tuning dataincluded in the first return path data, rectification of the secondreturn path data to improve operation of a computer determining mediaratings based on the second return path data by correcting for bias inthe second return path data.
 9. The non-transitory computer readablemedium of claim 8, wherein the first return path data is obtained from anon-panelist household, and the instructions cause the machine to:generate activity validation data based on third return path dataobtained from a panelist household; and when the start time and the endtime of the segment of tuning data for the first return path data arenot consistent, adjust the first return path data to include the validtuning data based on the activity validation data.
 10. Thenon-transitory computer readable medium of claim 6, wherein a first oneof the first viewing periods, associated with a first one of thehouseholds, covers multiple segments of tuning data, and theinstructions further cause the machine to: determine whether ones of themultiple segments of tuning data are associated with at least one tuningsource; when more than one of the multiple segments of tuning data isnot associated with at least one tuning source, compare a total tuningduration of the first one of the first viewing periods to mapped tuningduration of the first one of the first viewing periods, the mappedtuning duration corresponding to a duration of the ones of the multiplesegments of tuning data associated with at least one tuning source; anddetermine the first return path data includes the valid tuning databased on the comparison of the total tuning duration to the mappedtuning duration.
 11. An apparatus comprising: communications interfacecircuitry to receive, via a network, return path data from a pluralityof households that include set top boxes, the plurality of householdsassociated with a geographic region having a population such that thehouseholds number in at least the thousands; memory to store the returnpath data; instructions; and processor circuitry to execute theinstructions to: classify viewing periods, associated with respectiveones of the households, the viewing periods associated with one or moresegments of the return path data into two or more tuning classificationsbased on the one or more segments of the return path data; calculate atotal reported tuning duration for a first viewing period of the viewingperiods based on individual tuning durations for the first viewingperiod associated with respective ones of the two or more tuningclassifications; compare the total reported tuning duration to aduration threshold; and either retain or discard the return path dataassociated with the first view period based on the comparison to improveoperation of a computer determining media ratings by correcting for biasin the return path data.
 12. The apparatus of claim 11, wherein the twoor more tuning classifications includes at least one of live tuning orplayback tuning.
 13. The apparatus of claim 11, wherein the processorcircuitry is to: determine whether the return path data is associatedwith an error based on the comparison; retain the return path data whenthe return path data is not associated with the error; and discard thereturn path data when the return path data is associated with the error.14. The apparatus of claim 11, wherein the duration threshold is greaterthan a duration of the first viewing period.
 15. The apparatus of claim11, wherein the processor circuitry is to determine a number of tuningsources tuned by the set top boxes during the viewing periods based onthe one or more segments of return path data associated with the viewingperiods.
 16. The apparatus of claim 15, wherein the processor circuitryis to determine the number of tuning sources based on tuning sourcestuned by the set top boxes for at least a threshold portion of ones ofthe viewing periods.
 17. The apparatus of claim 15, wherein theprocessor circuitry is to classify the viewing periods as a conflictedtuning period when the number of tuning sources satisfies a tuningsource threshold.
 18. The apparatus of claim 17, wherein the processorcircuitry is to: classify multiple viewing periods associated withdifferent segments of the return path data from a first one of the settop boxes into the tuning classifications, the multiple viewing periodsincluding the first viewing period; and determine whether the returnpath data is useable based on a comparison of a proportion of themultiple viewing periods classified as conflicted tuning periods to aconflicted tuning threshold.
 19. The apparatus of claim 1, wherein thehierarchy of different tuning classifications defines a preference ofthe live tuning classification over the playback tuning classification.20. The apparatus of claim 1, wherein the hierarchy of different tuningclassifications defines a preference of the playback tuningclassification over a stand-by classification.